Save Data

file = os.environ['pydaddy_data_file'] delimiter = os.environ['pydaddy_data_delimiter'] fmt = os.environ['pydaddy_data_col_fmt'] data = np.loadtxt(file, delimiter=delimiter) x, y, t = decode_fmt(fmt) if t != None: t = data[:,t] else: t = os.environ['pydaddy_t']

Summary

| Mx range                       :   (-1.0, 1.0)  |
| Mx mean                        :      0.005     |
| My range                       :   (-1.0, 1.0)  |
| My mean                        :      0.02      |
| |M| range                      :  (0.002, 1.0)  |
| |M| mean                       :      0.778     |
| Autocorr time (Mx, My, |M^2|)  : (298, 264, 36) |
| (Dt, dt)                       :     (1, 1)     |

Drift

Diffusion

Cross Diffusion

Noise Diagnostics

Noise statistics:
Mean: (-0.0163, -0.0052)
Correlation matrix:
    +1.0000    +0.0405
    +0.0405    +1.0000

Noise autocorrelation time:
    eta_x: 0.020 timesteps (0.002s)    eta_y: 0.003 timesteps (0.000s)

Recovering functional forms for drift and diffusion

A1

(0.064 ± 0.003)x + (-0.123 ± 0.005)x^3 + (-0.119 ± 0.005)xy^2

A2

(0.065 ± 0.003)y + (-0.123 ± 0.006)x^2y + (-0.122 ± 0.005)y^3

B11

(0.050 ± 0.000) + (-0.045 ± 0.000)x^2 + (-0.050 ± 0.000)y^2

B22

(0.051 ± 0.000) + (-0.052 ± 0.000)x^2 + (-0.046 ± 0.000)y^2

B12

(0.003 ± 0.000)x^2 + (0.005 ± 0.000)xy + (-0.003 ± 0.000)y^2

B21

(0.003 ± 0.000)x^2 + (0.005 ± 0.000)xy + (-0.003 ± 0.000)y^2